Towards Exploring the Influence of Community Structures on Information Dissemination in Sina Weibo Networks

Abstract

The power of online social networks to propagate information within communities and from one community to the next is undeniable. Both network structure and information propagation affect each other; they restrict and cooperate with each other. However, they can also dynamically reshape the network topology of user’s social relationship in this process. The above process ultimately forms a feedback loop: the network structure affects how information spreads, while information propagation reshapes network topologies, so both evolve in concert over time. Using information propagation trees (IPT) of posts from the Sina Weibo microblogging site, we conducted a null model-based analysis to determine the influence of community structures on information propagation. We first generated randomized copies of the IPTs and then mined community structures from the originals and copies for comparison. An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.

SCWIR: A rumor propagation model considering user waiting behavior

“…With the surge in the number of social network users, user relationships show diversity, the clustering characteristics of social networks are increasingly prominent, and the role of group relationships in information dissemination and diffusion becomes more and more important (Zhang et al, 2021b;Zheng et al, 2023). In online social networks, users frequently participate in multiple closely related social groups, such as friends, colleagues, family members, etc., through which information can spread rapidly.…”